#  Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# import glob
import os

# import cv2
import numpy as np
import paddle
from PIL import Image

from .base_predictor import BasePredictor
from .models import DRNGenerator
from .utils.download import get_path_from_url
from .utils.logger import get_logger

# from tqdm import tqdm


# from .utils.video import frames2video, video2frames

REALSR_WEIGHT_URL = [
    "https://paddlegan.bj.bcebos.com/applications/DF2K_JPEG.pdparams",
    "https://paddlegan.bj.bcebos.com/models/esrgan_psnr_x4.pdparams",
    "https://paddlegan.bj.bcebos.com/models/lesrcnn_x4.pdparams",
    "https://paddlegan.bj.bcebos.com/models/DRNSx4.pdparams",
][-1]


class RealSRPredictor(BasePredictor):
    def __init__(self, output="output", weight_path=None):
        self.input = input
        self.output = os.path.join(output, "RealSR") if output else None
        self.model = DRNGenerator((2, 4), 30, 16, 3, 255, 0.2)
        if weight_path is None:
            weight_path = get_path_from_url(REALSR_WEIGHT_URL)

        state_dict = paddle.load(weight_path)
        try:
            self.model.load_dict(state_dict["generator"])
        except:
            self.model.load_dict(state_dict)
        self.model.eval()

    def norm(self, img):
        img = np.array(img).transpose([2, 0, 1]).astype("float32") / 255.0
        return img.astype("float32")

    def denorm(self, img):
        img = img.transpose((1, 2, 0))
        return (img * 255).clip(0, 255).astype("uint8")

    def run_image(self, img):
        if isinstance(img, str):
            ori_img = Image.open(img).convert("RGB")
        elif isinstance(img, np.ndarray):
            ori_img = Image.fromarray(img).convert("RGB")
        elif isinstance(img, Image.Image):
            ori_img = img

        img = self.norm(ori_img)
        x = paddle.to_tensor(img[np.newaxis, ...])
        with paddle.no_grad():
            out = self.model(x)

        pred_img = self.denorm(out[2].numpy()[0])  # 0：1倍，1：2倍，2：4倍
        pred_img = Image.fromarray(pred_img)
        return pred_img

    # def run_video(self, video):
    #     base_name = os.path.basename(video).split('.')[0]
    #     output_path = os.path.join(self.output, base_name) if self.output else None
    #     pred_frame_path = os.path.join(output_path, 'frames_pred')

    #     if not os.path.exists(output_path):
    #         os.makedirs(output_path)

    #     if not os.path.exists(pred_frame_path):
    #         os.makedirs(pred_frame_path)

    #     cap = cv2.VideoCapture(video)
    #     fps = cap.get(cv2.CAP_PROP_FPS)

    #     out_path = video2frames(video, output_path)

    #     frames = sorted(glob.glob(os.path.join(out_path, '*.png')))

    #     for frame in tqdm(frames):
    #         pred_img = self.run_image(frame)

    #         frame_name = os.path.basename(frame)
    #         pred_img.save(os.path.join(pred_frame_path, frame_name))

    #     frame_pattern_combined = os.path.join(pred_frame_path, '%08d.png')

    #     vid_out_path = os.path.join(output_path,
    #                                 '{}_realsr_out.mp4'.format(base_name))
    #     frames2video(frame_pattern_combined, vid_out_path, str(int(fps)))

    #     return frame_pattern_combined, vid_out_path

    def run(self, input):
        if self.output:
            if not os.path.exists(self.output):
                os.makedirs(self.output)

        # if not self.is_image(input):
        #     return self.run_video(input)
        # else:
        pred_img = self.run_image(input)

        out_path = None
        if self.output:
            try:
                base_name = os.path.splitext(os.path.basename(input))[0]
            except:
                base_name = "result"
            out_path = os.path.join(self.output, base_name + ".jpg")
            pred_img.save(out_path)
            logger = get_logger()
            logger.info("Image saved to {}".format(out_path))

        return pred_img, out_path
